Contagion dynamics on higher-order networks

TL;DR

This paper reviews the emerging field of contagion dynamics on higher-order networks, highlighting new models, their similarities, differences, and the need for empirical validation to advance understanding of spreading processes.

Contribution

It provides a unified formalism for various contagion models on higher-order networks and discusses future research directions for theoretical and empirical studies.

Findings

Different functional forms for spreading dynamics identified

Unified formalism proposed for most models

Empirical validation remains limited

Abstract

Understanding the dissemination of diseases, information, and behavior stands as a paramount research challenge in contemporary network and complex systems science. The COVID-19 pandemic and the proliferation of misinformation are relevant examples of the importance of these dynamic processes, which have recently gained more attention due to the potential of higher-order networks to unlock new avenues for their investigation. Despite being in its early stages, the examination of social contagion in higher-order networks has witnessed a surge of novel research and concepts, revealing different functional forms for the spreading dynamics and offering novel insights. This review presents a focused overview of this body of literature and proposes a unified formalism that covers most of these forms. The goal is to underscore the similarities and distinctions among various models, to motivate further research on the general and universal properties of such models. We also highlight that while the path for additional theoretical exploration appears clear, the empirical validation of these models through data or experiments remains scant, with an unsettled roadmap as of today. We therefore conclude with some perspectives aimed at providing possible research directions that could contribute to a better understanding of this class of dynamical processes, both from a theoretical and a data-oriented point of view.